



import json
import os
import pickle
import shutil
import tempfile
from unittest import TestCase
from unittest.mock import patch

import numpy as np
from datasets import Dataset

from transformers import is_faiss_available
from transformers.models.bart.configuration_bart import BartConfig
from transformers.models.bart.tokenization_bart import BartTokenizer
from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES as DPR_VOCAB_FILES_NAMES
from transformers.models.dpr.configuration_dpr import DPRConfig
from transformers.models.dpr.tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer
from transformers.models.rag.configuration_rag import RagConfig
from transformers.models.rag.retrieval_rag import CustomHFIndex, RagRetriever
from transformers.models.roberta.tokenization_roberta import VOCAB_FILES_NAMES as BART_VOCAB_FILES_NAMES
from transformers.testing_utils import require_faiss, require_sentencepiece, require_tokenizers, require_torch


if is_faiss_available():
			import faiss


@require_faiss
class 						lowercase    (							_A):
						'''simple docstring'''


						def       lowerCamelCase_				(  self    :				Union[str, Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				tempfile.mkdtemp()
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				8

											# DPR tok
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				[
											    '[UNK]',
											    '[CLS]',
											    '[SEP]',
											    '[PAD]',
											    '[MASK]',
											    'want',
											    '##want',
											    '##ed',
											    'wa',
											    'un',
											    'runn',
											    '##ing',
											    ',',
											    'low',
											    'lowest',
											]
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				os.path.join(self.tmpdirname       ,	'dpr_tokenizer'	)
											os.makedirs(UpperCamelCase__       ,	exist_ok=UpperCamelCase__	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				os.path.join(UpperCamelCase__       ,	DPR_VOCAB_FILES_NAMES['vocab_file']	)
											with open(self.vocab_file       ,	'w'       ,	encoding='utf-8'	) as vocab_writer:
																vocab_writer.write(''.join([x + '\n' for x in vocab_tokens]	)	)

											# BART tok
											SCREAMING_SNAKE_CASE							:     int						=				[
											    'l',
											    'o',
											    'w',
											    'e',
											    'r',
											    's',
											    't',
											    'i',
											    'd',
											    'n',
											    '\u0120',
											    '\u0120l',
											    '\u0120n',
											    '\u0120lo',
											    '\u0120low',
											    'er',
											    '\u0120lowest',
											    '\u0120newer',
											    '\u0120wider',
											    '<unk>',
											]
											SCREAMING_SNAKE_CASE							:     Any						=				dict(zip(UpperCamelCase__       ,	range(len(UpperCamelCase__	)	)	)	)
											SCREAMING_SNAKE_CASE							:     int						=				['#version: 0.2', '\u0120 l', '\u0120l o', '\u0120lo w', 'e r', '']
											SCREAMING_SNAKE_CASE							:     List[str]						=				{'unk_token': '<unk>'}

											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				os.path.join(self.tmpdirname       ,	'bart_tokenizer'	)
											os.makedirs(UpperCamelCase__       ,	exist_ok=UpperCamelCase__	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				os.path.join(UpperCamelCase__       ,	BART_VOCAB_FILES_NAMES['vocab_file']	)
											SCREAMING_SNAKE_CASE							:     Tuple						=				os.path.join(UpperCamelCase__       ,	BART_VOCAB_FILES_NAMES['merges_file']	)
											with open(self.vocab_file       ,	'w'       ,	encoding='utf-8'	) as fp:
																fp.write(json.dumps(UpperCamelCase__	) + '\n'	)
											with open(self.merges_file       ,	'w'       ,	encoding='utf-8'	) as fp:
																fp.write('\n'.join(UpperCamelCase__	)	)







						def       lowerCamelCase_				(  self    :				List[str]	):



											'''simple docstring'''


											return DPRQuestionEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,	'dpr_tokenizer'	)	)







						def       lowerCamelCase_				(  self    :				Tuple	):



											'''simple docstring'''


											return DPRContextEncoderTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,	'dpr_tokenizer'	)	)







						def       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											return BartTokenizer.from_pretrained(os.path.join(self.tmpdirname       ,	'bart_tokenizer'	)	)







						def       lowerCamelCase_				(  self    :				Optional[int]	):



											'''simple docstring'''


											shutil.rmtree(self.tmpdirname	)







						def       lowerCamelCase_				(  self    :				List[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				Dataset.from_dict(
											    {
											        'id': ['0', '1'],
											        'text': ['foo', 'bar'],
											        'title': ['Foo', 'Bar'],
											        'embeddings': [np.ones(self.retrieval_vector_size	), 2 * np.ones(self.retrieval_vector_size	)],
											    }	)
											dataset.add_faiss_index('embeddings'       ,	string_factory='Flat'       ,	metric_type=faiss.METRIC_INNER_PRODUCT	)
											return dataset







						def       lowerCamelCase_				(  self    :				Union[str, Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     str						=				self.get_dummy_dataset()
											SCREAMING_SNAKE_CASE							:     str						=				RagConfig(
											    retrieval_vector_size=self.retrieval_vector_size       ,	question_encoder=DPRConfig().to_dict()       ,	generator=BartConfig().to_dict()       ,	)
											with patch('transformers.models.rag.retrieval_rag.load_dataset'	) as mock_load_dataset:
																SCREAMING_SNAKE_CASE							:     int						=				dataset
																SCREAMING_SNAKE_CASE							:     Optional[Any]						=				RagRetriever(
																    UpperCamelCase__       ,	question_encoder_tokenizer=self.get_dpr_tokenizer()       ,	generator_tokenizer=self.get_bart_tokenizer()       ,	)
											return retriever







						def       lowerCamelCase_				(  self    :				List[str]       ,	snake_case    :				bool	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				self.get_dummy_dataset()
											SCREAMING_SNAKE_CASE							:     Any						=				RagConfig(
											    retrieval_vector_size=self.retrieval_vector_size       ,	question_encoder=DPRConfig().to_dict()       ,	generator=BartConfig().to_dict()       ,	index_name='custom'       ,	)
											if from_disk:
																SCREAMING_SNAKE_CASE							:     int						=				os.path.join(self.tmpdirname       ,	'dataset'	)
																SCREAMING_SNAKE_CASE							:     str						=				os.path.join(self.tmpdirname       ,	'index.faiss'	)
																dataset.get_index('embeddings'	).save(os.path.join(self.tmpdirname       ,	'index.faiss'	)	)
																dataset.drop_index('embeddings'	)
																dataset.save_to_disk(os.path.join(self.tmpdirname       ,	'dataset'	)	)
																del dataset
																SCREAMING_SNAKE_CASE							:     str						=				RagRetriever(
																    UpperCamelCase__       ,	question_encoder_tokenizer=self.get_dpr_tokenizer()       ,	generator_tokenizer=self.get_bart_tokenizer()       ,	)
											else:
																SCREAMING_SNAKE_CASE							:     Any						=				RagRetriever(
																    UpperCamelCase__       ,	question_encoder_tokenizer=self.get_dpr_tokenizer()       ,	generator_tokenizer=self.get_bart_tokenizer()       ,	index=CustomHFIndex(config.retrieval_vector_size       ,	UpperCamelCase__	)       ,	)
											return retriever







						def       lowerCamelCase_				(  self    :				List[str]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[str]						=				Dataset.from_dict(
											    {
											        'id': ['0', '1'],
											        'text': ['foo', 'bar'],
											        'title': ['Foo', 'Bar'],
											        'embeddings': [np.ones(self.retrieval_vector_size + 1	), 2 * np.ones(self.retrieval_vector_size + 1	)],
											    }	)
											dataset.add_faiss_index('embeddings'       ,	string_factory='Flat'       ,	metric_type=faiss.METRIC_INNER_PRODUCT	)

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				os.path.join(self.tmpdirname       ,	'hf_bert_base.hnswSQ8_correct_phi_128.c_index'	)
											dataset.save_faiss_index('embeddings'       ,	index_file_name + '.index.dpr'	)
											pickle.dump(dataset['id']       ,	open(index_file_name + '.index_meta.dpr'       ,	'wb'	)	)

											SCREAMING_SNAKE_CASE							:     List[str]						=				os.path.join(self.tmpdirname       ,	'psgs_w100.tsv.pkl'	)
											SCREAMING_SNAKE_CASE							:     List[str]						=				{sample['id']: [sample['text'], sample['title']] for sample in dataset}
											pickle.dump(UpperCamelCase__       ,	open(UpperCamelCase__       ,	'wb'	)	)

											SCREAMING_SNAKE_CASE							:     str						=				RagConfig(
											    retrieval_vector_size=self.retrieval_vector_size       ,	question_encoder=DPRConfig().to_dict()       ,	generator=BartConfig().to_dict()       ,	index_name='legacy'       ,	index_path=self.tmpdirname       ,	)
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				RagRetriever(
											    UpperCamelCase__       ,	question_encoder_tokenizer=self.get_dpr_tokenizer()       ,	generator_tokenizer=self.get_bart_tokenizer()	)
											return retriever







						def       lowerCamelCase_				(  self    :				List[str]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				1
											SCREAMING_SNAKE_CASE							:     Any						=				self.get_dummy_canonical_hf_index_retriever()
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				np.array(
											    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]       ,	dtype=np.floataa	)
											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Optional[int]						=				retriever.retrieve(UpperCamelCase__       ,	n_docs=UpperCamelCase__	)
											self.assertEqual(retrieved_doc_embeds.shape       ,	(2, n_docs, self.retrieval_vector_size)	)
											self.assertEqual(len(UpperCamelCase__	)       ,	2	)
											self.assertEqual(sorted(doc_dicts[0]	)       ,	['embeddings', 'id', 'text', 'title']	)
											self.assertEqual(len(doc_dicts[0]['id']	)       ,	UpperCamelCase__	)
											self.assertEqual(doc_dicts[0]['id'][0]       ,	'1'	)  # max inner product is reached with second doc
											self.assertEqual(doc_dicts[1]['id'][0]       ,	'0'	)  # max inner product is reached with first doc
											self.assertListEqual(doc_ids.tolist()       ,	[[1], [0]]	)







						def       lowerCamelCase_				(  self    :				Tuple	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     str						=				self.get_dummy_canonical_hf_index_retriever()
											with tempfile.TemporaryDirectory() as tmp_dirname:
																with patch('transformers.models.rag.retrieval_rag.load_dataset'	) as mock_load_dataset:
																					SCREAMING_SNAKE_CASE							:     List[Any]						=				self.get_dummy_dataset()
																					retriever.save_pretrained(UpperCamelCase__	)
																					SCREAMING_SNAKE_CASE							:     Tuple						=				RagRetriever.from_pretrained(UpperCamelCase__	)
																					self.assertIsInstance(UpperCamelCase__       ,	UpperCamelCase__	)
																					SCREAMING_SNAKE_CASE							:     Any						=				np.array(
																					    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]       ,	dtype=np.floataa	)
																					SCREAMING_SNAKE_CASE							:     List[str]						=				retriever.retrieve(UpperCamelCase__       ,	n_docs=1	)
																					self.assertTrue(out is not None	)







						def       lowerCamelCase_				(  self    :				List[str]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				1
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase__	)
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				np.array(
											    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]       ,	dtype=np.floataa	)
											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				retriever.retrieve(UpperCamelCase__       ,	n_docs=UpperCamelCase__	)
											self.assertEqual(retrieved_doc_embeds.shape       ,	(2, n_docs, self.retrieval_vector_size)	)
											self.assertEqual(len(UpperCamelCase__	)       ,	2	)
											self.assertEqual(sorted(doc_dicts[0]	)       ,	['embeddings', 'id', 'text', 'title']	)
											self.assertEqual(len(doc_dicts[0]['id']	)       ,	UpperCamelCase__	)
											self.assertEqual(doc_dicts[0]['id'][0]       ,	'1'	)  # max inner product is reached with second doc
											self.assertEqual(doc_dicts[1]['id'][0]       ,	'0'	)  # max inner product is reached with first doc
											self.assertListEqual(doc_ids.tolist()       ,	[[1], [0]]	)







						def       lowerCamelCase_				(  self    :				Any	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase__	)
											with tempfile.TemporaryDirectory() as tmp_dirname:
																retriever.save_pretrained(UpperCamelCase__	)
																SCREAMING_SNAKE_CASE							:     Any						=				RagRetriever.from_pretrained(UpperCamelCase__	)
																self.assertIsInstance(UpperCamelCase__       ,	UpperCamelCase__	)
																SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				np.array(
																    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]       ,	dtype=np.floataa	)
																SCREAMING_SNAKE_CASE							:     Any						=				retriever.retrieve(UpperCamelCase__       ,	n_docs=1	)
																self.assertTrue(out is not None	)







						def       lowerCamelCase_				(  self    :				Optional[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				1
											SCREAMING_SNAKE_CASE							:     str						=				self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase__	)
											SCREAMING_SNAKE_CASE							:     Any						=				np.array(
											    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]       ,	dtype=np.floataa	)
											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Dict						=				retriever.retrieve(UpperCamelCase__       ,	n_docs=UpperCamelCase__	)
											self.assertEqual(retrieved_doc_embeds.shape       ,	(2, n_docs, self.retrieval_vector_size)	)
											self.assertEqual(len(UpperCamelCase__	)       ,	2	)
											self.assertEqual(sorted(doc_dicts[0]	)       ,	['embeddings', 'id', 'text', 'title']	)
											self.assertEqual(len(doc_dicts[0]['id']	)       ,	UpperCamelCase__	)
											self.assertEqual(doc_dicts[0]['id'][0]       ,	'1'	)  # max inner product is reached with second doc
											self.assertEqual(doc_dicts[1]['id'][0]       ,	'0'	)  # max inner product is reached with first doc
											self.assertListEqual(doc_ids.tolist()       ,	[[1], [0]]	)







						def       lowerCamelCase_				(  self    :				str	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     Tuple						=				self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase__	)
											with tempfile.TemporaryDirectory() as tmp_dirname:
																retriever.save_pretrained(UpperCamelCase__	)
																SCREAMING_SNAKE_CASE							:     Any						=				RagRetriever.from_pretrained(UpperCamelCase__	)
																self.assertIsInstance(UpperCamelCase__       ,	UpperCamelCase__	)
																SCREAMING_SNAKE_CASE							:     List[Any]						=				np.array(
																    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]       ,	dtype=np.floataa	)
																SCREAMING_SNAKE_CASE							:     List[str]						=				retriever.retrieve(UpperCamelCase__       ,	n_docs=1	)
																self.assertTrue(out is not None	)







						def       lowerCamelCase_				(  self    :				List[Any]	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[str]						=				1
											SCREAMING_SNAKE_CASE							:     List[str]						=				self.get_dummy_legacy_index_retriever()
											SCREAMING_SNAKE_CASE							:     List[Any]						=				np.array(
											    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]       ,	dtype=np.floataa	)
											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     str						=				retriever.retrieve(UpperCamelCase__       ,	n_docs=UpperCamelCase__	)
											self.assertEqual(retrieved_doc_embeds.shape       ,	(2, n_docs, self.retrieval_vector_size)	)
											self.assertEqual(len(UpperCamelCase__	)       ,	2	)
											self.assertEqual(sorted(doc_dicts[0]	)       ,	['text', 'title']	)
											self.assertEqual(len(doc_dicts[0]['text']	)       ,	UpperCamelCase__	)
											self.assertEqual(doc_dicts[0]['text'][0]       ,	'bar'	)  # max inner product is reached with second doc
											self.assertEqual(doc_dicts[1]['text'][0]       ,	'foo'	)  # max inner product is reached with first doc
											self.assertListEqual(doc_ids.tolist()       ,	[[1], [0]]	)







						def       lowerCamelCase_				(  self    :				Tuple	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     List[Any]						=				self.get_dummy_legacy_index_retriever()
											with tempfile.TemporaryDirectory() as tmp_dirname:
																retriever.save_pretrained(UpperCamelCase__	)
																SCREAMING_SNAKE_CASE							:     Optional[int]						=				RagRetriever.from_pretrained(UpperCamelCase__	)
																self.assertIsInstance(UpperCamelCase__       ,	UpperCamelCase__	)
																SCREAMING_SNAKE_CASE							:     List[str]						=				np.array(
																    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]       ,	dtype=np.floataa	)
																SCREAMING_SNAKE_CASE							:     Tuple						=				retriever.retrieve(UpperCamelCase__       ,	n_docs=1	)
																self.assertTrue(out is not None	)







						@require_torch
						@require_tokenizers
						@require_sentencepiece
						def       lowerCamelCase_				(  self    :				str	):



											'''simple docstring'''


											import torch

											SCREAMING_SNAKE_CASE							:     int						=				1
											SCREAMING_SNAKE_CASE							:     Optional[int]						=				self.get_dummy_canonical_hf_index_retriever()
											SCREAMING_SNAKE_CASE							:     int						=				[[5, 7], [10, 11]]
											SCREAMING_SNAKE_CASE							:     Dict						=				np.array(
											    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]       ,	dtype=np.floataa	)
											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				retriever(UpperCamelCase__       ,	UpperCamelCase__       ,	prefix=retriever.config.generator.prefix       ,	n_docs=UpperCamelCase__	)
											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Any						=				(
											    out['context_input_ids'],
											    out['context_attention_mask'],
											    out['retrieved_doc_embeds'],
											)
											self.assertEqual(retrieved_doc_embeds.shape       ,	(2, n_docs, self.retrieval_vector_size)	)
											self.assertIsInstance(UpperCamelCase__       ,	UpperCamelCase__	)
											self.assertIsInstance(UpperCamelCase__       ,	UpperCamelCase__	)
											self.assertIsInstance(UpperCamelCase__       ,	np.ndarray	)

											SCREAMING_SNAKE_CASE							:     Optional[Any]						=				retriever(
											    UpperCamelCase__       ,	UpperCamelCase__       ,	prefix=retriever.config.generator.prefix       ,	n_docs=UpperCamelCase__       ,	return_tensors='pt'       ,	)
											SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE					,  SCREAMING_SNAKE_CASE							:     Any						=				(  # noqa: F841
											    out['context_input_ids'],
											    out['context_attention_mask'],
											    out['retrieved_doc_embeds'],
											    out['doc_ids'],
											)
											self.assertEqual(retrieved_doc_embeds.shape       ,	(2, n_docs, self.retrieval_vector_size)	)
											self.assertIsInstance(UpperCamelCase__       ,	torch.Tensor	)
											self.assertIsInstance(UpperCamelCase__       ,	torch.Tensor	)
											self.assertIsInstance(UpperCamelCase__       ,	torch.Tensor	)







						@require_torch
						@require_tokenizers
						@require_sentencepiece
						def       lowerCamelCase_				(  self    :				int	):



											'''simple docstring'''


											SCREAMING_SNAKE_CASE							:     int						=				self.get_dpr_ctx_encoder_tokenizer()
											SCREAMING_SNAKE_CASE							:     str						=				1
											SCREAMING_SNAKE_CASE							:     Union[str, Any]						=				self.get_dummy_custom_hf_index_retriever(from_disk=UpperCamelCase__	)
											retriever.set_ctx_encoder_tokenizer(UpperCamelCase__	)

											SCREAMING_SNAKE_CASE							:     Optional[int]						=				[[5, 7], [10, 11]]
											SCREAMING_SNAKE_CASE							:     List[Any]						=				np.array(
											    [np.ones(self.retrieval_vector_size	), -np.ones(self.retrieval_vector_size	)]       ,	dtype=np.floataa	)
											SCREAMING_SNAKE_CASE							:     List[str]						=				retriever(UpperCamelCase__       ,	UpperCamelCase__       ,	prefix=retriever.config.generator.prefix       ,	n_docs=UpperCamelCase__	)

											self.assertEqual(
											    len(UpperCamelCase__	)       ,	6	)  # check whether the retriever output consist of 6 attributes including tokenized docs
											self.assertEqual(
											    all(k in out for k in ('tokenized_doc_ids', 'tokenized_doc_attention_mask')	)       ,	UpperCamelCase__	)  # check for doc token related keys in dictionary.

from ...configuration_utils import PretrainedConfig
from ...utils import logging


__lowerCAmelCase     :	Dict       			=  logging.get_logger(__name__)

__lowerCAmelCase     :	int       			=  {
    'microsoft/trocr-base-handwritten': (
        'https://huggingface.co/microsoft/trocr-base-handwritten/resolve/main/config.json'
    ),
    # See all TrOCR models at https://huggingface.co/models?filter=trocr
}




class 			UpperCAmelCase_     (					_A	):


  '''simple docstring'''







  a__       			=						"""trocr"""
  a__       			=						["""past_key_values"""]
  a__       			=						{
      """num_attention_heads""": """decoder_attention_heads""",
      """hidden_size""": """d_model""",
      """num_hidden_layers""": """decoder_layers""",
  }
  def __init__(					self     :					Dict     ,				UpperCamelCase__     :					Optional[Any]=5_0265     ,				UpperCamelCase__     :					int=1024     ,				UpperCamelCase__     :					int=12     ,				UpperCamelCase__     :					List[Any]=16     ,				UpperCamelCase__     :					Union[str, Any]=4096     ,				UpperCamelCase__     :					List[Any]="gelu"     ,				UpperCamelCase__     :					Optional[int]=512     ,				UpperCamelCase__     :					Optional[Any]=0.1     ,				UpperCamelCase__     :					int=0.0     ,				UpperCamelCase__     :					str=0.0     ,				UpperCamelCase__     :					Optional[int]=2     ,				UpperCamelCase__     :					List[str]=0.02     ,				UpperCamelCase__     :					Dict=0.0     ,				UpperCamelCase__     :					List[str]=True     ,				UpperCamelCase__     :					str=False     ,				UpperCamelCase__     :					List[str]=True     ,				UpperCamelCase__     :					Tuple=True     ,				UpperCamelCase__     :					str=1     ,				UpperCamelCase__     :					Union[str, Any]=0     ,				UpperCamelCase__     :					List[str]=2     ,				**UpperCamelCase__     :					Tuple     ,				)     ->      Dict:







    """simple docstring"""






    __magic_name__								=			vocab_size
    __magic_name__								=			d_model
    __magic_name__								=			decoder_layers
    __magic_name__								=			decoder_attention_heads
    __magic_name__								=			decoder_ffn_dim
    __magic_name__								=			activation_function
    __magic_name__								=			max_position_embeddings
    __magic_name__								=			dropout
    __magic_name__								=			attention_dropout
    __magic_name__								=			activation_dropout
    __magic_name__								=			init_std
    __magic_name__								=			decoder_layerdrop
    __magic_name__								=			use_cache
    __magic_name__								=			scale_embedding
    __magic_name__								=			use_learned_position_embeddings
    __magic_name__								=			layernorm_embedding

    super().__init__(
        pad_token_id=UpperCamelCase__     ,				bos_token_id=UpperCamelCase__     ,				eos_token_id=UpperCamelCase__     ,				decoder_start_token_id=UpperCamelCase__     ,				**UpperCamelCase__     ,				)

